Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

Omics profile of iPSC-derived astrocytes from Progressive Supranuclear Palsy (PSP) patients

INTRODUCTION

Progressive Supranuclear Palsy (PSP) is a neurodegenerative tauopathy and, to date, the pathophysiological mechanisms in PSP that lead to Tau hyperphosphorylation and neurodegeneration are not clear. In some brain areas, Tau pathology in glial cells appears to precede Tau aggregation in neurons. The development of a model using astrocyte cell lines derived from patients has the potential to identify molecules and pathways that contribute to early events of neurodegeneration. We developed a model of induced pluripotent stem cells (iPSC)-derived astrocytes to investigate the pathophysiology of PSP, particularly early events that might contribute to Tau hyperphosphorylation, applying omics approach to detect differentially expressed genes, metabolites, and proteins, including those from the secretome.

METHODS

Skin fibroblasts from PSP patients (without MAPT mutations) and controls were reprogrammed to iPSCs, further differentiated into neuroprogenitor cells (NPCs) and astrocytes. In the 5th passage, astrocytes were harvested for total RNA sequencing. Intracellular and secreted proteins were processed for proteomics experiments. Metabolomics profiling was obtained from supernatants only.

RESULTS

We identified hundreds of differentially expressed genes. The main networks were related to cell cycle re-activation in PSP. Several proteins were found exclusively secreted by the PSP group. The cellular processes related to the cell cycle and mitotic proteins, TriC/CCT pathway, and redox signaling were enriched in the secretome of PSP. Moreover, we found distinct sets of metabolites between PSP and controls.

CONCLUSION

Our iPSC-derived astrocyte model can provide distinct molecular signatures for PSP patients and it is useful to elucidate the initial stages of PSP pathogenesis.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.